Skip to main content Accessibility help
×
Home

Comparison of different imputation methods from low- to high-density panels using Chinese Holstein cattle

  • Z. Weng (a1), Z. Zhang (a1) (a2), Q. Zhang (a1), W. Fu (a1), S. He (a1) and X. Ding (a1)...

Abstract

Imputation of high-density genotypes from low- or medium-density platforms is a promising way to enhance the efficiency of whole-genome selection programs at low cost. In this study, we compared the efficiency of three widely used imputation algorithms (fastPHASE, BEAGLE and findhap) using Chinese Holstein cattle with Illumina BovineSNP50 genotypes. A total of 2108 cattle were randomly divided into a reference population and a test population to evaluate the influence of the reference population size. Three bovine chromosomes, BTA1, 16 and 28, were used to represent large, medium and small chromosome size, respectively. We simulated different scenarios by randomly masking 20%, 40%, 80% and 95% single-nucleotide polymorphisms (SNPs) on each chromosome in the test population to mimic different SNP density panels. Illumina Bovine3K and Illumina BovineLD (6909 SNPs) information was also used. We found that the three methods showed comparable accuracy when the proportion of masked SNPs was low. However, the difference became larger when more SNPs were masked. BEAGLE performed the best and was most robust with imputation accuracies >90% in almost all situations. fastPHASE was affected by the proportion of masked SNPs, especially when the masked SNP rate was high. findhap ran the fastest, whereas its accuracies were lower than those of BEAGLE but higher than those of fastPHASE. In addition, enlarging the reference population improved the imputation accuracy for BEAGLE and findhap, but did not affect fastPHASE. Considering imputation accuracy and computational requirements, BEAGLE has been found to be more reliable for imputing genotypes from low- to high-density genotyping platforms.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Comparison of different imputation methods from low- to high-density panels using Chinese Holstein cattle
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Comparison of different imputation methods from low- to high-density panels using Chinese Holstein cattle
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Comparison of different imputation methods from low- to high-density panels using Chinese Holstein cattle
      Available formats
      ×

Copyright

The online version of this article is published within an Open Access environment subject to the conditions of the Creative Commons Attribution-NonCommercial-ShareAlike licence . The written permission of Cambridge University Press must be obtained for commercial re-use.

Corresponding author

E-mail: xding@cau.edu.cn

References

Hide All
Berry, DP, Kearney, JF 2011. Imputation of genotypes from low- to high-density genotyping platforms and implication for genomic selection. Animal 10, 18.
Boichard, D, Chung, H, Dassonneville, R, David, X, Eggen, A, Fritz, S, Gietzen, KJ, Hayes, BJ, Lawley, CT, Sonstegard, TS, Van Tassell, CP, VanRaden, PM, Viaud-Martinez, KA, Wiggans, GR 2012. Design of a bovine low-density SNP array optimized for imputation. PLoS ONE 7, e34130.
Browning, SR, Browning, BL 2007. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. American Journal of Human Genetics 81, 10841097.
Druet, T, Farnir, FP 2011. Modeling of identity-by-descent processes along a chromosome between haplotypes and their genotyped ancestors. Genetics 188, 409419.
Druet, T, Georges, M 2010. A Hidden Markov Model combining linkage and linkage disequilibrium information for haplotype reconstruction and quantitative trait locus fine mapping. Genetics 184, 789798.
Druet, T, Schrooten, C, de Roos, AP 2010. Imputation of genotypes from different single nucleotide polymorphism panels in dairy cattle. Journal of Dairy Science 93, 54435454.
Li, Y, Willer, C, Sanna, S, Abecasis, G 2009. Genotype imputation. Annual Review of Genomics and Human Genetics 10, 387406.
Li, Y, Willer, CJ, Ding, J, Scheet, P, Abecasis, GR 2010. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genetic Epidemiology 34, 816834.
Marchini, J, Howie, B 2010. Genotype imputation for genome-wide association studies. Nature Review Genetics 11, 499511.
Marchini, J, Howie, B, Myers, S, McVean, G, Donnelly, P 2007. A new multipoint method for genome-wide association studies by imputation of genotypes. Nature Genetics 39, 906913.
Marshall, TC, Slate, J, Kruuk, LEB, Pemberton, JM 1998. Statistical confidence for likelihood-based paternity inference in natural populations. Molecular Ecology 7, 639655.
Nothnagel, M, Ellinghaus, D, Schreiber, S, Krawczak, M, Franke, A 2009. A comprehensive evaluation of SNP genotype imputation. Human Genetics 125, 163171.
Pei, YF, Li, J, Zhang, L, Papasian, CJ, Deng, HW 2008. Analyses and comparison of accuracy of different genotype imputation methods. PLoS ONE 3, e3551.
Qin, ZS, Niu, T, Liu, JS 2002. Partition-ligation-expectation-maximization algorithm for haplotype inference with single-nucleotide polymorphisms. American Journal of Human Genetics 71, 12421247.
Scheet, P, Stephens, M 2006. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. American Journal of Human Genetics 78, 629644.
Shriner, D, Adeyemo, A, Chen, G, Rotimi, CN 2010. Practical considerations for imputation of untyped markers in admixed populations. Genetic Epidemiology 34, 258265.
VanRaden, PM, O'Connell, JR, Wiggans, GR, Weigel, KA 2011. Genomic evaluations with many more genotypes. Genetic Selection Evolution 43, 10.
Weigel, KA, Van Tassell, CP, O'Connell, JR, VanRaden, PM, Wiggans, GR 2010. Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms. Journal of Dairy Science 93, 22292238.
Yu, Z, Schaid, DJ 2007. Methods to impute missing genotypes for population data. Human Genetics 122, 495504.
Zhang, Z, Druet, T 2010. Marker imputation with low-density marker panels in Dutch Holstein cattle. Journal of Dairy Science 93, 54875494.

Keywords

Comparison of different imputation methods from low- to high-density panels using Chinese Holstein cattle

  • Z. Weng (a1), Z. Zhang (a1) (a2), Q. Zhang (a1), W. Fu (a1), S. He (a1) and X. Ding (a1)...

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed